Method and mechanism for assisted diagnosis and maintenance of health monitoring system

ABSTRACT

The invention relates to a system and method of a health monitoring network which automates detection of faulty or failed sensors using real-time fault checking on a dynamically registered sensor data stream. The monitoring system and sensor network can provide a one-touch system to notify users when a sensor requires attention, without prior knowledge of the operational characteristics, installation method or configuration of sensors in the network. The network uses a decision engine to assist in maintenance according to a profile based on individual preferences and capabilities.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of and claims the benefitunder 35 U.S.C. 120 of co-pending U.S. application Ser. No. 11/646,599,filed on Dec. 28, 2006, the entire content of which is incorporatedherein by reference.

FIELD OF THE INVENTION

The invention relates to a system and method for a health monitoringnetwork which automates detection of faulty or failed sensors, assistsin the maintenance of, based on customizable profiles preferences andcapabilities.

BACKGROUND

Existing solutions are designed with limited run time to avoid the needfor unsupervised system maintenance. Administration requires expertiseand training in the system. Additionally, work on multi-sensor healthmonitoring systems focuses on communication methods, data analysis, andtransducer efficacy. Practical issues in multi-sensor deployment,especially multi-sensor on-body deployments such as maintaining amulti-sensor system with zero-training and minimal inconvenience arevirtually non-existent.

Common methods include some form of the BIST (built-in self test) onsensor nodes, and manual inspection of data during installation andafter loss of data is detected. The execution or interpretation ofresults from these methods requires technical knowledge that most userslack. Finally, there are substantial periods of missing data when manualinspections are not regularly conducted.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary embodiment in accordance with theembodiments of the invention.

FIG. 2 illustrates an exemplary flow between the base state, messagesand sensors in accordance with an embodiment of the invention.

FIG. 3 illustrates an exemplary flow diagram of a continuous loop basedon sample rate.

FIG. 4 illustrates an exemplary flow of connection, registration anddata transfer for the sensor.

FIG. 5 illustrates an exemplary flow of data between the base stationand sensor when neither are connected.

FIG. 6 illustrates an exemplary flow of data between the base stationand sensor when the base station is connected and the sensor is notconnected.

FIG. 7 illustrates an exemplary flow of data between the base stationand sensor when both are connected and data transfer occurs.

DETAILED DESCRIPTION

The embodiments of the invention relates a system for maintaining asensor network, the sensor network comprising at least one sensor; abase station adapted to receive data from the sensor network; at leastone sensor being adapted to communicate data to the base station; afault-detection system adapted to analyze data from the at least onesensor and determine an operating state of the at least one sensor,wherein the fault-detection system is located in the base station or ona remote computing resource; and a decision engine embedded in the basestation or on the remote computing source, the decision engine beingadapted to activate a sensor attention indicator based on an output fromthe fault-detection system indicative of an operating state of the atleast one sensor. Preferably, the sensor network is adapted for healthmonitoring sensor network.

Yet other embodiments of the invention relate to a method formaintaining a sensor network, comprising providing the sensor networkcomprising at least one sensor; providing a base station adapted toreceive data from the sensor network; the at least one sensor beingadapted to communicate data to the base station; providing afault-detection system adapted to analyze data from the at least onesensor and determine an operating state of the at least one sensor,wherein the fault-detection system is located in the base station or ona remote computing resource; and providing a decision engine embedded inthe base station or on the remote computing source, the decision enginebeing adapted to activate a sensor attention indicator based on anoutput from the fault-detection system indicative of an operating stateof the at least one sensor.

A “sensor network” is a network of autonomous devices deployed tocollect data with some means to aggregate the data collected. A healthmonitoring sensor network is a sensor network where the goal is tocollect data relevant to health issues including (a) wearablepatient/user monitoring: psychological (ECG, heart rate etc.) orbehavioral (movement, socialization, object use, etc.); (b) occupantmonitoring from a fixed location in a building; and (c) environmentalmonitoring, for example, in changes in condition in the localenvironment (temperature, air quality, etc.).

FIG. 1 shows the time-sequence of events of one embodiment for thesystem and demonstrates how a user could use the system with littletraining to perform a potentially complex sensor network maintenanceprocedure. The elements are a base station with a user button labeled,“Assist”, and many sensors with indicators (initially inactive as shownby the grey circles) and a consumable component such as battery voltagerepresented as a fluid level.

From an initial point in time, the user then employs the invention forassistance in maintaining the sensor network. To do so the user pressesthe Assist button on the base-station. The base-station then uses thedecision engine (in this case a remote resource as indicated by thecloud) to compute the correct assistance output. In this case, anindicator is activated on the sensor with the lowest consumable level(battery voltage). To complete maintenance, the user could place asensor on the base-station where a consumable resource of the sensor(such battery charge) could be replenished.

Below the schematic of FIG. 1, a pseudo-code is provided to explain theoperation of the decision engine for a useful scenario, namely,immediately indicating any sensor faults or in absence of a fault,selecting the best single sensor from a multitude of sensors formaintenance.

FIG. 2 shows a time-sequence of data-checking aspects of the inventionin operation. First the sensor sends a message to the base-station toregister its data output. The message includes a valid output range—forsimplicity, in this case, the decision engine could be rule-based andmerely checks incoming data to ensure that it is within a range. Thebase-station could respond to the sensor with an acknowledgement messagethat signifies a successful sensor registration. A series of data valuescould then be transmitted. For example, the first three could be withinrange, while the next exceeds the upper bound on the range. Thebase-station responds with an error signal that actuates an alert on thesensor. The actuation could be indicated by a color change on the sensornode which may correspond to the color of an LED indicator.

FIG. 3 provides a visual diagram of the operating states in a sensor asan aid in understanding the power computations and equations asapplicable to the embodiments of this invention. For example, the sensorcould operate in a loop of 3 states: Setup, Acquire, and Idle. In theSetup state sensor application and peripheral components are configured.For example, an analog to digital conversion is enabled for one or moreinput channels at a set sampling frequency. In the Acquire state, datais acquired (moved into memory). Typical sources for acquisition includeperipherals with a digital interface, or an analog to digital converter.These sources could be integrated within a single IC, or one or moremodules (printed circuit boards). In the Idle state, sensor elements aredisabled and powered down to maximize runtime. FIG. 3 also shows aperiodic Store or Communicate state. It is within this state thatcommunication with the base-station occurs. The communication may be aninput to the fault detector or an output that actuates a sensorindicator. Time spent in each state is required to be not equal, but maybe so, and will depend on data rates and communication requirements.

FIG. 4 shows a view of the states that comprise the communicate actionthat a sensor may perform once operational but not engaged in a dataacquisition. From the “IDLE-Connected/-Registered” state—meaning thatthe device is neither connected nor registered with a base station—thesensor first initiates a connection with a base station. Uponconnection, the sensor enters the next IDLE state, from which it willattempt to register itself with the base station; once registered, thesensor will begin transferring status and/or acquisition data to thebase station. This transfer may involve multiple iterations of thetransitions between “IDLE Connected/Registered” and “DATA XFER,” asnecessary to move all required information packets and to allow forinterceding acquisitions. Upon completion of data transfer transaction,the sensor will notify the base station that its transfer is complete bysending a de-registration message; at this point the sensor will dropits connection to the base station as well.

FIG. 5 breaks out the detail of the communications during the“IDLE-Connected/-Registered” state between the base station and thesensor. The sensor initiates a conversation by sending a request for aconnection; the base station responds with an affirmativeacknowledgement “ACK.” An “ACK” formally establishes the connected statebetween the two devices, whereupon the sensor can then make thetransition to the next state “IDLE Connected/-Registered.”

FIG. 6 shows detail of the communications involved in the registrationprocess between the base station and the sensor in the “IDLEConnected/-Registered” state. Similar to the connection process, thebase station awaits a registration packet from the sensor after the twodevices establish a connection. Once this packet is received andprocessed, the base station replies with an “ACK.” The devices are nowprepared to enter a data transfer cycle.

FIG. 7 details the communications between the base station and thesensor once the sensor has connected and registered with the basestation, and the two devices are in the data transfer process. Anindividual transfer is triggered by the sensor sending a data packet tothe base station. The base station validates the data format and/or datavalues sent, then responds with either an “ACK” or an error message.Either response allows the sensor to respond with—in the case of an“ACK”—another data packet, or a de-registration packet if it hastransferred all of its data. If the base station sends an error signal,then the sensor can either re-send the data packet based upon somerecovery constraint—the figure shows a attempt counter, but anothermetric may be employed—or enter an error/alarm condition by notifyingthe base station, thereby de-registering and entering the“IDLE-Connected/-Registered” state.

The sensors could have several different configurations. One feature ofthe sensor configuration includes a coordinating base-station (alsocommonly called an aggregation device or sensor gateway) with theability for user to invoke assistance in maintaining a sensor network;ability to communicate with sensors to determine status of consumablessuch as power and chemical agents and verify signal quality from affixedsensors that might “fall off” during activity; and physical interface torefresh depleted sensor resources.

Another feature of the sensor configuration includes one or more sensorswith ability to communicate status to coordinating base-station (asdescribed above) and ability to attract user attention such as ablinking indicator or audible alarm.

Yet another feature of the sensor configuration includes decision enginewhich is an application running on base-station or remotely via calls tothe base station that can (1) access sensor status information; (2)activate sensor attention indicators based on a stored profile; and (3)be customized according to health care provider or patient preferences,capabilities of the sensor and the intended monitoring plan.

Some of the applications of the sensor network of the embodiments of theinvention include detection of a fall by a person; determining medicineefficacy; detecting a change in behavioral or physiological behaviorassociated with management or new diagnosis of a disease; activitylogging; detecting a change in behavior with no proven association witha disease state, e.g., performance training and personal journaling.

The method and apparatus of the embodiments of the invention havegreater relevance and utility in a multi-sensor network with ability forbase-station to communicate with the sensor nodes of the sensor network.Preferably, the sensor network is wireless. For example, a healthmonitoring sensor network could use a wireless communication method thatis either standards based or proprietary to establish, exercise, andmaintain communication.

In the embodiments of the invention, a sensor includes a transducer andthe appropriate computation or electronics to encode or digitize theoutput of the transducer and satisfy wireless communicationrequirements. For example, in one embodiment the sensor could use amicrocontroller to store and later transmit data, or stream data to awireless transceiver. If the transducer has a digital output, then thetransducer may be directly connected to the microcontroller. If thetransducer has an analog output, a signal conditioning front end andanalog to digital converter would be generally required.

Another feature of the sensor network of the embodiments of theinvention is the base station, which generally has the followingcomponents: (1) method for user to invoke assistance—typically a button;(2) device for computation (microcontroller/CPU) to meet applicationrequirements; (3) wireless communication hardware and communicationsoftware (network stack, firmware, etc.); (4) ability to determinesensor status and verify signal quality; and (5) interface to refreshdepleted sensor resources. One example is a plug, connector, or powercoupling system to re-charge sensor batteries. Another example concernsa sensor that has an element with operating lifetime limits such as anadhesive, or a chemical transducer that “expires” once activity—the basestation would have an interface to assist with refreshing thoseresources.

There may be several base stations in a single physical location such asa home and sensor network could support mobility (roaming betweenmultiple base-stations). The adaptation of multiple base-stations in asensor network is merely choice of correctly using known computingcomponents including software to meet functional requirements, andsatisfactory human-factors and industrial engineering to meet userrequirements.

In one embodiment, software could check several base-stations. Forexample, base stations could pass data to another system such as aservice provider's backend to provide ability to check several basestations. The components of the software could include known methods andarchitectures for scalable computer systems could be used. Preferably,the base-station is not user configurable beyond the initial setup,which is analogous to other network appliances (home routers, wirelessaccess points, cable modems, etc.).

In yet other embodiments, the system monitors a component of the atleast one sensor that has a service life based on a number ofactivations, a duration of activation, or an operating condition.Preferably, the base-station has a capability to reset or replenish thecomponent that limits service life to enable continuous operation of thehealth monitoring system. In accordance with these embodiments, theoperational lifetime limit such as duration of activation which might bemeasured as a number of hours elapsed. An example would be that of anadhesive mount. Another example is the duration of time betweentransducer or sensor calibration. The number of activations would applyto a lifetime limit for number of uses. This may for example be due tochemical agents used in a sensor or based on finite and countableconsumable supply used in the sensor, for example test strips in amedical monitor. The operating condition would describe datasheet valuesfor temperature, shock, etc. that must be followed for the transducer tooperate as designed.

Furthermore, the base-station could be adapted to be used by employing asingle user button to initiate maintenance of a health monitoringsystem. For example, see FIG. 1 and the explanation on this figure, butin short this feature relates to the ability by which a single buttoncould invoke the decision engine which would then compute the requiredoutput of the system.

In the embodiments of the invention, the base-station is adapted to sendand receive data. The protocol for sending and receiving data aredescribed below. For example, FIGS. 4-5 show how one might register asensor with the base-station in, and FIG. 2 shows how the base-stationmight then check the operating status of the sensor and also checkquality of sensor data. Furthermore, the sensors are adapted tocommunicate data to base-station, for example, the sensor cancommunicate and measure certain parameters (e.g., battery voltage).

Another feature of the sensor network of the embodiments of theinvention is a fault-detection system that could be hardware, softwareor a combination thereof, but preferably is a software system. The faultdetection system is for detecting a fault in the network system. Once afault has been identified, the rectification would then generally happenat the base-station (for low-battery, or a depleted resource) orreplacement of failed sensor device. The fault-detection system coulddetect when thresholds are exceeded (time, or a measured parameter)and/or detect when data is out of range of normal range that could bebased on methods of data analysis and inference such as rules/decisiontree, statistical methods, or physical modeling.

Another function of the sensor network of the embodiments of theinvention is to detect sensors that are at the end of operational life,failed transducers or other fragile components, and failed placement ofsensors.

Preferably, the fault-detection system is adapted to analyze data fromthe sensor and determine an operating state of the sensor, i.e., whetherthe sensor is operating normally or abnormally. The fault-detectionsystem could be located in the base station or on a remote computingresource. The remote computing resource would generally not be at thehome; it would be elsewhere in the world network, e.g., hosted by aservice provider. In some embodiments of the invention, there could bemultiple remote computing resources that communicate with the sensornetwork. However, generally, the remote computing resources are notconfigurable by the user, but can be configured by the service providerwho maintains the remote computing resource.

Yet another feature of the sensor networks of the embodiments of theinvention relates to a decision engine, which could be more than one persensor network, but preferably there is one decision engine per sensornetwork. The decision engine causes output action (actuation) based onone or more system inputs. The decision engine could be adapted toactivate a sensor attention indicator, e.g., a visual indicator such asdisplay screen, lamps, LEDs; an audio indicator such as speaker output,buzzer, or chirper; or a tactile indicator preferably having avibration.

In one embodiment of the invention, the base station bi-directionallycommunicates with the at least one sensor. In yet another embodiment,the sensor system is adapted to notify a detected fault on the at leastone sensor by notifying an user and/or a system administrator ormanagement professional. The notification can be real-time or scheduleddepending on user or administrator preferences.

In one embodiment of the invention, the base station hosts a secure,remotely-configurable bridge to the sensor network. This could be doneby a standard protocol, typically IP (internet protocol), and associatedextensions for secure sessions. Other methods of connectivity suitablefor bridging to the sensor network include telephone-modem (POTS),cellular or satellite data connections, and WLAN or long-range wirelesscommunication.

Preferably, the sensor system has a sensor that captures data from anaccelerometer, an optical motion detector, or an object motion detectorsuch as a vibration or tilt transducer.

In one embodiment the sensor system has a sensor that captures data froma MEMs accelerometer.

In another embodiment the sensor system has a sensor that captures datafrom a PIR (passive infra-red) motion detector.

In one embodiment of the invention, upon initialization of communicationbetween the base station and the at least one sensor, the system isadapted to start a registration process.

Furthermore, in the embodiments of the invention, at least one sensorcould be adapted to send header information to the base stationincluding at least one of the following: information about data-type forscalar quantity measure by the device, valid range, units for thescalar, frequency of delivery, and a descriptive character string; andthe base station could be adapted to reply with an acknowledgementmessage. Preferably, upon receipt of an error signal from the basestation, the at least one sensor actuates a detectable alarm.Preferably, the decision engine includes a user stored profile based ondata supplied by a healthcare provider or a patient to allowcustomization of the decision engine to meet capabilities andrequirements of patients (users). For example, a 50 year old with mildheart disease would have different capabilities then a 90 year old withdementia from Alzheimer's.

In yet another aspect of the invention, and upon initialization ofcommunication between the base station and the at least one sensor, aregistration process occurs (see FIG. 6).

In one aspect of the invention, the registration process comprises theat least one sensor sending header information to the base stationincluding at one of the following: information about data-type forscalar quantity measure by the device, valid range, units for thescalar, frequency of delivery, and a descriptive character string; andthe base station replies with an acknowledgement message (see FIG. 6).

In another aspect of the invention, and upon receipt of an error signalfrom the base station, the at least one sensor actuates a userdetectable alarm (see FIG. 7).

In still another aspect of the invention, the decision engine iscustomizable and includes a user stored profile based on data suppliedby a healthcare provider or patient sensing requirements.

Exemplary elements of the invention are:

-   A) Coordinating base-station with:    -   1) Method for user to invoke assistance in maintaining a sensor        network.    -   2) Ability to communicate with sensors to determine status of        consumables such as power and chemical agents and verify signal        quality from affixed sensors that might “fall off” during        activity.    -   3) Physical interface to refresh depleted sensor resources.-   B) Wearable sensors with:    -   1) Ability to communicate status to coordinating base-station        (as described above).    -   2) Ability to attract user attention such as a blinking        indicator or audible alarm.-   C) Decision engine that can:    -   1) Access sensor status information.    -   2) Activate sensor attention indicators based on a stored        profile.    -   3) Be customized according to health care provider or patient        preferences, capabilities of the sensor and the intended        monitoring plan

In another embodiment of the invention, there is a method to provideassistance in the maintenance of a health monitoring system byautomating detection of faulty or failed sensors using real-timefault-checking on a dynamically registered sensor data stream. Themonitoring system and sensor network can provide a one-touch system tonotify users when a sensor requires attention. The user need not haveprior knowledge of the operational characteristics, installation method,or configuration of sensors in the network to unambiguously detect afault.

Additional exemplary elements are:

-   -   A base station that has the ability to conduct bi-directional        communications with sensors in the health monitoring system.        Data from each sensor is presented to a fault-detection service        for checking and the results are used to notify users of faults        on specific sensors at an opportune time. The base station hosts        a secure, remotely-configurable bridge to the local health        monitoring network.    -   A number of portable sensors that can, upon initialization,        notify the base station of the nature of their data        transmissions, and accept a message from the base station that        triggers either a visible or audible trouble indicator.    -   A fault-detection service that can select and apply        data-checking based on a library of methods associated with        specific registered data types and available computational        resources.

The embodiments of the invention address the problem(s) of:

-   -   Deploying and maintaining a long-term multi-sensor system        offering new diagnostic and intervention options for a wide        range of conditions. The adoption of these systems requires new        methods of automated assistance because in many cases the target        population has limited knowledge of technology, physical or        cognitive impairment, and limited patience for sensing systems        that further marginalize their quality of life.    -   The diminutive form-factor of on-body sensors in multi-sensor        systems requires regular intervention to maintain effectiveness        in long-term monitoring scenarios. In a multi-sensor system the        complexity of maintenance is multiplied and will require new        methods of user assistance and deployment.    -   Self-powered sensors located around users (environmental        sensors) offer advantages for mass-casualty events and other        temporary deployments. Minimizing maintenance overhead while        ensuring reliable operation is a strong product advantage.    -   The invention provides a method of assisted maintenance based on        system components that are well understood.    -   The embodiments of the invention simplify the maintenance of a        health monitoring sensor system by autonomously validating data        from a dynamic group of homogeneous or heterogeneous sensors,        and provides a facile method that requires no user training for        locating a faulty sensor.    -   Examples of fault conditions include:        -   1) Sensors that are dislodged from their mounting point.        -   2) Sensors with consumable properties or limited operating            lifetime.        -   3) Sensors that become inoperable due to power transients or            corrupted memory.        -   4) Sensors that become inoperable due to tampering by            visitors, pets or pests.    -   For long term sensing, the method and mechanism described will        limit the burden on the patient according to a customizable        profile that guides a decision engine. The system will assist        the user in preventative maintenance of the sensor network at        the user's request. This method is easily adapted into the        patient's daily routine.    -   The direct result will be longer periods of multi-sensor data        capture on a wider range of individuals.    -   The consumable aspects of sensor design can be implemented with        less design margin. Relaxing design margins will result in        smaller, simpler and less expensive sensor systems providing        competitive advantage over multi-sensor systems that do not use        the techniques described.    -   The system ensures robust data collection in a dynamic sensing        environment by allowing technically naïve users to isolate        faults to a specific sensor.    -   The system relieves sensor devices with modest processing        resources of the burden of self-diagnosis.    -   The system is extensible and supports remote management and        customization.

EXAMPLES

An exemplary embodiment of one detailed application relates to the caseof a multi-limb motion capture system for physical rehabilitation, suchas stroke recovery. Referring to the embodiment in FIG. 1, ten sensorswith 7-day battery life and recording capability are placed on the body(one for each limb segment and two on the torso). Data fromaccelerometers in the sensor will be used to form a detailed model ofbody kinetics to guide treatment. The data set created by monitoring thesensors will be especially valuable since the user will be performingnatural activities. However, maintaining ten sensors, for example for2-3 weeks, is burdensome—the sensors look the same and are easilyconfused. Without assistance the user will be forced to charge all tenblindly every few nights to ensure battery life. The charging apparatuswill be bulky and intimidating.

To assist the user and dramatically improve the usability of the sensorsystem, the proposed methods are employed using at least the followingdecision engine rules:

-   -   Ensure sensors have safety margin in remaining battery life; and    -   Indicate best sensor for charging when prompted by user for        maintenance assistance.

The patient can now maintain the sensing system, for example, based on asimple set of instructions:

-   -   1) Every night when you remove the sensors before sleep, go to        the small charging base-station and press the button.    -   2) Place the sensor with an indicator illuminated in the        base-station for maintenance (in this case data transfer and        battery charging).

Over, for example, the three week period of monitoring as describedabove, the sensors are rotated through the base-station and the simplemaintenance procedure becomes second nature much like charging acellular phone.

Referring to the embodiment in FIGS. 5 and 6, the base station acts asserver to sensor clients, which perform a two-step registration processduring initialization of communications. The client sends headerinformation to the base station including information includingdata-type for scalar quantity measured by the device, valid range, unitsfor the scalar, frequency of delivery, and a descriptive characterstring. The base station replies with an acknowledgement message.

The base station calls a fault detection service to check sensor datafor proper operation. A simple example would be to perform temporal andscalar bounds-checking for each sensor's data stream with aberrantvalues measured against the device's recent historical trend. A morecomplex system may use modeling and machine learning techniques toqualify the sensed data using additional data sources (time of day,season, etc.)

Sensors can be quite different or have subtle failure modes makingmanual checking tedious for humans, but the process is easily automatedusing the method herein described.

The fault detection service can be hosted in the base station or on aremote computing resource depending on the processing requirements.

The sensor acts as client to base station server, but listens forspurious messages from server. After registration, sensor minimizescommunications to data transmission. Upon receipt of an error signalfrom base station, the sensor actuates a human-detectable alarm, forexample, a low power blinking LED. A one-touch-for-maintenance systemmay log a sensor fault but wait for a user button-press to signal afault.

Referring to the tables below, one method of sensor operating lifecalculation is to use average current during a repeated sampling windowaccording to the following equation:IAvg=(tStore×IStore)+(tComm×IComm)+[(tWindow−Store−tComm)×IAcq)]  (1)

MEMs accelerometers are useful sensors. The following table waspopulated based on the operating specifications of an ultra-low powerCPU acquiring 16 bit samples for X-Y-Z data using DMA and power savingmodes (subject to timing constraints).

Sampling Rate (Hz), use IAcq (mA) Bits per second 10, activitysegmentation  .090  480 50, gesture recognition .44 2400 100, clinicalgrade .90 4800 measurement

Using conservative values of 20 mA for IStore (better thenstate-of-the-art for reliable standards based wireless communication orflash media) and an aggressive transfer rate of 0.5 Mbps the followingtable can be computed using the previous table and equation 1. The tableshows best case sensor operating life for 4 common form factors:

Sampling Rate Button, Key-Fob, Pager, Phone, (Hz) tStore(s) IAvg (mA)100 mAh 250 mAh 500 mAh 1000 mAh 10 .001 .12 35 days 87 days 174 days350 days 50 .005 .30 14 days 35 days  69 days 140 days 100 .01 .59  7days 18 days  35 days  70 days

Adding extra degrees of sensing will result in even shorter operatinglife—e.g. ½ as long when collecting all 6-axis of motion.

Returning to the case of a clinical grade gait and motion capture systemusing ten sensors, each sensor would require a 500 mAh battery for onemonth of data capture. The proposed system for assisted maintenancewould allow the user to continuously rotate through considerably smallersensors without guidance from a caregiver. For example, a key-fob sizeddevice would provide margin and a single sensor could be recharged everynight. If the sensor system requires wireless communication, real-worlddata transfer rates can be up to 100× worse then the numbers above dueto packet loss

Alternatively, in a preferred embodiment with reference to the tableabove and FIG. 7, sensor operation is repetitive and can be expressed asa repetitive loop consisting of states for data acquisition, datastorage, data communication, and sensor idle. Over time, the averagepower consumed by the sensor will be equal to a sum of terms for powerin each state weighted by time spent in each state. In the likely worstcase, current draw for each state can be determined from a static valuefrom device datasheets.

For battery powered devices, power is approximated by=Current draw on abattery with a manufacturer's mAh rating. Therefore power in a state isapproximated by current, not current*voltage.

From these assertions, an equation for average current in a repetitiveloop is derived:Average Current/Many Identical Loops˜=Current/LoopAverage Current=[Time spent storing*Current while storing]+[Time spentcommunicating*Current while communicating]+[Time spent in setup toacquiring data*Current in setup to acquiring data]+[Time spent acquiringdata*Current while acquiring data]+[Time spent Idle*Current while Idle]IAvg=[tStore*IStore]+[tComm*IComm]+[tSetup*ISetup]+[tAcq*IAcq]+[tIdle*IIdle]  (1)

It can be observed that:tLoop=tStore+tComm+tSetup+tAcq+tIdle  (2)

To simplify notation, an average current for Setup, Acquire and Idlestates could be calculated with 100 Hz Sampling:

The acquisition process uses data from datasheets for timingrequirements and current consumption (e.g., 1.3mA=ICPUinDMA+IADC+IAccelerometer) and involves the following states:

1) Idle;

2) Enable on the accelerometer so that power can stabilize (setup toacquiring data); and

3) Enable the ADC and sample X/Y/Z data points.

The number in the time row is the sample period, the number below is thecalculated average current.

Bits per second can be calculated easily:Samples per second*bits per sample*channels sampled=bits per second.

-   -   For 10Hz:        10*16*3=480 bps

For 50 Hz, the value is 2400 bps

For 100 Hz the value is 4800 bps

From this we get the completed first and second tables above.

So:IAvg=[tStore*IStore]+[0*IComm]+[tSampling*ISampling]  (2)

-   -   See Continuous Loop based on Sample Rate (FIG. 7).        tStore=Bits per second/data transfer rate.    -   tSampling and ISample are calculated based on the sample rate        chart above.

The second table above is based on plugging results to equation (1)using Istore=20 ma and a data transfer rate of 0.5 Mbps. Also tComm isset to zero since Istore is less then IComm in most cases.

-   -   For 10 Hz and communication every second:        Iavg=((0.001 s)*20 mA)+(1 s−0.001 s)*0.091 mA=0.11 mA    -   For 50 Hz and communication every second:        Iavg=((0.005 s)*20 mA)+(1 s−0.005 s)*0.44 mA=0.53 mA    -   For 100 Hz and communication every second:        Iavg=((0.010 s)*20 mA)+(1 s−0.010 s)*0.88 mA=1.1 mA    -   Finally, battery life is calculated using the formula:        Operating life (hrs)=[(Battery capacity (mAh)/Iavg)]        Operating life days=Operating life hrs/24    -   For 10 Hz:        100 mAh/0.11 ma=909 hrs=38 days        250 mAh/0.11 ma=2272 hrs=95 days        500 mAh 0.11 ma=4545 hrs=189 days        1000 mAh/0.11 ma=9091 hrs=379 days    -   For 50 Hz:        100 mAh=8 days        250 mAh=20 days        500 mAh=39. days        1000 mAh=79 days    -   For 100 Hz:        100 mAh=4 days        250 mAh=10 days        500 mAh=19 days        1000 mAh=38 days

Additional advantages of this invention will become readily apparent tothose skilled in this art from the following detailed description,wherein only the preferred embodiments of this invention is shown anddescribed, simply by way of illustration of the best mode contemplatedfor carrying out this invention. As will be realized, this invention iscapable of other and different embodiments, and its details are capableof modifications in various obvious respects, all without departing fromthis invention. Accordingly, the drawings and description are to beregarded as illustrative in nature and not as restrictive. Whileembodiments of the invention have been particularly shown and describedwith references to embodiments thereof, it will be understood by thoseskilled in the art that various changes in form and details may be madetherein without departing from the scope of embodiments of the inventionencompassed by the appended claims.

This application discloses several numerical ranges in the text andfigures. The numerical ranges disclosed support any range or valuewithin the disclosed numerical ranges even though a precise rangelimitation is not stated verbatim in the specification because thisinvention can be practiced throughout the disclosed numerical ranges. Inthe claims, the terms “a” and “an” mean one or more. Finally, the entiredisclosure of the patents and publications referred in this applicationare hereby incorporated herein by reference.

1. A system, comprising: a base station adapted to wirelessly receivedata from a first sensor, the base station comprising: a fault detectionunit adapted to detect a faulty operating state of the first sensorbased on whether a value of the data received from the first sensor iswithin a predefined sensor data value range stored on the base station,based on a frequency of delivery of the sensor data on a time of day thesensor data was received from the first sensor, a season in which thesensor data was received from the first sensor, based on estimatingremaining sensor operating life or based on any combination thereof, anda decision engine unit adapted to cause the base station to wirelesslytransmit an attention indicator activation command to the first sensorto activate an attention indicator on the first sensor based on thedetected faulty operating state of the first sensor.
 2. The system ofclaim 1, wherein the fault detection unit is adapted to determine theoperating state of the first sensor based on a frequency of delivery ofthe sensor data.
 3. The system of claim 1, wherein the fault detectionunit is adapted to estimate the remaining sensor operating life based ona sensor current consumption rate value stored on the base station andto determine the operating state of the first sensor based on theestimated remaining sensor operating life.
 4. The system of claim 1,wherein the base station is adapted to host a secure,remotely-configurable bridge to the sensor network.
 5. The system ofclaim 1, wherein the base station further comprises a user inputconfigured to trigger the fault detection unit to determine whether thefirst sensor needs maintenance.
 6. The system of claim 1, wherein thefirst sensor comprises an accelerometer, a motion detector, or objectmotion detector.
 7. The system of claim 1, wherein upon initializationof communication between the base station and the first sensor, thesystem is adapted to start a registration process.
 8. The system ofclaim 1, wherein: the first sensor is adapted to send header informationto the base station including at least one of the following: informationabout data-type for scalar quantity(s) measured by the device, validrange, units for the scalar, frequency of delivery, and a descriptivecharacter string; and the base station is adapted to reply with anacknowledgement message.
 9. The system of claim 1, wherein the decisionengine unit includes a user stored profile based on data supplied by ahealthcare provider or a patient.
 10. A method, comprising: receivingwirelessly, at a base station, data from a first sensor; detecting, atthe base station, a faulty operating state of the first sensor based onwhether a value of the data received from the first sensor is within apredefined sensor data value range stored on the base station, based ona frequency of delivery of the sensor data based on a time of day thesensor data was received from the first sensor, a season in which thesensor data was received from the first sensor, based on estimatingremaining sensor operating life station, or based on any combinationthereof; and transmitting wirelessly, from the base station to the firstsensor to activate an attention indicator on the first sensor, anattention indicator activation command based on the detected faultyoperating state of the first sensor.
 11. The method of claim 10, whereinthe fault detection unit is adapted to determine the operating state ofthe first sensor based on a frequency of delivery of the sensor data.12. The method of claim 10, wherein estimating the remaining sensoroperating life is based on a sensor current consumption rate valuestored on the base station, and wherein determining the operating stateof the first sensor is based on the estimated remaining sensor operatinglife.
 13. The method of claim 10, wherein the base station is adapted tohost a secure, remotely-configurable bridge to the sensor network. 14.The method of claim 10, wherein the base station further comprises auser input configured to trigger the fault detection unit to determinewhether the first sensor needs maintenance.
 15. The method of claim 10,wherein the first sensor comprises an accelerometer, a motion detector,or object motion detector.
 16. The method of claim 10, wherein uponinitialization of communication between the base station and the firstsensor, the system is adapted to start a registration process.
 17. Themethod of claim 16, wherein the registration process comprises: sending,via the first sensor, header information to the base station includingat least one of the following: information about data-type for scalarquantity measure by the device, valid range, units for the scalar,frequency of delivery, and a descriptive character string; and replying,via the base station, with an acknowledgement message.
 18. The method ofclaim 10, wherein the base station comprises a power charging interfaceconfigured to provide power to the first sensor.
 19. The method of claim10, wherein the decision engine unit includes a stored user profilebased on data supplied by a healthcare provider or a patient.
 20. Thesystem of claim 1, wherein the base station is adapted to monitor acomponent of the first sensor that has a service life based on a numberof activations, a duration of activation, or an operating condition. 21.The system of claim 5, wherein the user input is configured to triggerthe fault detection unit to determine whether a battery of the firstsensor needs to be charged.
 22. The system of claim 20, wherein thebase-station has a capability to reset or replenish the component thatlimits service life to enable continuous operation of the healthmonitoring system.
 23. A system, comprising: a sensor network comprisinga first sensor; a base station adapted to wirelessly receive data fromthe first sensor to activate an attention indicator on the first sensor,the base station comprising: a fault detection unit adapted to detect afaulty operating state of the first sensor based on whether a value ofthe data received from the first sensor is within a predefined sensordata value range stored on the base station, based on a frequency ofdelivery of the sensor data based on a time of day the sensor data wasreceived from the first sensor, a season in which the sensor data wasreceived from the first sensor, based on a sensor current consumptionrate value stored on the base station, or based on any combinationthereof, and a decision engine unit adapted to cause the base station totransmit an attention indicator activation command to the first sensorbased on the detected faulty operating state of the first sensor.
 24. Amethod, comprising: transmitting, from a first sensor of a sensornetwork, data to a base station; receiving wirelessly, at a, basestation, data from the first sensor; detecting, at the base station, afaulty operating state of the first sensor based on whether a value ofthe data received from the first sensor is within a predefined sensordata value range stored on the base station, based on a frequency ofdelivery of the sensor data based on a time of day the sensor data wasreceived from the first sensor, a season in which the sensor data wasreceived from the first sensor, based on a sensor current consumptionrate value stored on the base station, or based on any combinationthereof; and transmitting from the base station to the first sensor toactivate an attention indicator on the first sensor, an attentionindicator activation command based on the detected faulty operatingstate of the first sensor.
 25. The system of claim 22, wherein thecapability to rest or replenish the component that limits service lifecomprises a power charging interface configured to provide power to thefirst sensor.
 26. The system of claim 20 wherein the component of thefirst sensor being monitored comprises a battery.